Enhanced salp swarm algorithm based on convolutional neural network optimization for automatic epilepsy detection

DWI SUNARYONO; RIYANARTO SARNO; JOKO SISWANTORO; AGUS BUDI RAHARJO; SHOFFI IZZA SABILLA; RAHADIAN INDARTO SUSILO; KANA REKHA

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Enhanced salp swarm algorithm based on convolutional neural network optimization for automatic epilepsy detection

Author(s)

DWI SUNARYONO Department of Computer Science, Faculty of Intelligent Electrical and Informatics Technology, Kampus ITS Sukolilo, Surabaya 60111, Indonesia

RIYANARTO SARNO Department of Computer Science, Faculty of Intelligent Electrical and Informatics Technology, Kampus ITS Sukolilo, Surabaya 60111, Indonesia

JOKO SISWANTORO Department of Informatics Engineering, University of Surabaya, Jl. Kali Rungkut, Surabaya 60293 Indonesia

AGUS BUDI RAHARJO Department of Computer Science, Faculty of Intelligent Electrical and Informatics Technology, Kampus ITS Sukolilo, Surabaya 60111, Indonesia

SHOFFI IZZA SABILLA Department of Medical Technology, Faculty of Intelligent Electrical and Informatics Technology, Kampus ITS Sukolilo, Surabaya 60111, Indonesia

RAHADIAN INDARTO SUSILO Department of Neurosurgery, dr. Soetomo Academic General Hospital, Surabaya 60264, Indonesia

KANA REKHA Department of Neurosurgery, dr. Soetomo Academic General Hospital, Surabaya 60264, Indonesia

Abstract

Epilepsy is a neurological disorder that occurs due to abnormal activity in the brain. Symptoms can vary, such as uncontrolled movements, muscle stiffness, difficulty breathing, loss of consciousness, and even death. Therefore, the multichannel electroencephalogram (EEG) is very important to understand the pattern of seizure occurrence and non-seizure in epilepsy. In this paper, we determine an automatic epilepsy detection method using enhanced Salp Swarm Algorithm (SSA) CNN-based of EEG signals. The signal is transformed into Low Pass Filter (LPF) and High Pass Filter (HPF) with one level, frequencies, and scales using Wavelet Transform. Enhanced SSA was used to determine the number of neurons and the appropriate number of convolution layers in the CNN algorithm for classifying two classes (epilepsy and epilepsy with seizure) using the CHB-MIT dataset from Children's Hospital Boston. The results of the study show that the proposed method produces the highest accuracy of 99.15% and 89.04% of average accuracy. This result is obtained with a computation time on testing data of 0.0001 seconds using a high-end computer. Enhanced SSA was proven to increase the performance of CNN of 81.13%. The proposed method can be used in the automatic detection of epilepsy.

Publication Details

Page(s) 5615-5623

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 19, Year: 2022

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Keywords

Epilepsy, Convolutional Neural Network, Wavelet Transform, CHB-MIT, Salp Swarm Algorithm

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